Defining Glaucoma

An approach to glaucoma pathogenesis

Harry A. Quigley

Glaucoma Service and Dana Center for Preventive Ophthalmology, Wilmer Ophthalmological Institute, Johns Hopkins University School of Medicine, Baltimore, MD, USA

Introduction

Most of those interested in the pathogenesis of glaucoma have abandoned simplistic scenarios in which glaucoma is either ‘mechanical’ or ‘vascular’, though anachronistic thinking persists. Like all diseases, glaucoma results from many factors, and we must take new approaches to cause and effect that will improve investigations into its treatment. Glaucoma is a heterogeneous disorder characterized by progressive loss of mid-peripheral visual function, associated with excavation of the optic disc. From angle closure to exfoliation, from senile sclerotic to traumatic, there are many entry points to this definition. Yet, for 70 million or so persons with glaucoma worldwide, there are enough common factors in optic nerve damage that their pathogenesis can be discussed together.

Glaucoma primarily causes the death of retinal ganglion cells (RGC), though additional loss of their synaptic partners in the brain also occurs. In normal eyes without glaucoma, RGC die with age, at about 2500 RGC per year to the age of 50 years, accelerating to 7500/year thereafter.1 RGC death in glaucoma exceeds the average loss due to aging, and only when loss exceeds 30% of the cells in any one area does functional loss become measurable by present visual field tests.2 A modest proportion of all those with open-angle glaucoma (OAG) becomes legally blind during their lifetime, i.e., less than 10%. But, the disease is so prevalent that the absolute number of sufferers who are blind causes it to be ranked as the second most frequent blinding condition worldwide.1

The linear causation pathway

Previous conceptualizations of glaucoma pathogenesis were both simple and linear. One cause of glaucoma was championed (e.g., mechanical compression of nerve fibers or insufficiency of RGC vascular nutrition) and vigorous arguments were constructed to make the ‘other’ concept impossible. Even when more than

Address for correspondence: Harry A. Quigley, MD, Wilmer 120, Johns Hopkins Hospital, 600 North Wolfe Street, Baltimore, MD 21287, USA. e-mail: hquigley@jhmi.edu

Glaucoma in the New Millennium, pp. 3–12

Proceedings of the 50th Annual Symposium of the New Orleans Academy of Ophthalmology, New Orleans, LA, USA, April 6-8, 2001

edited by Jonathan Nussdorf

© 2003 Kugler Publications, The Hague, The Netherlands

4 H.A. Quigley

one possible cause was considered as an option, it was proposed that one cause always preceded the other. For example, a simple, linear formulation might propose that the intraocular pressure (IOP) compresses nerve head capillaries, causing a failure of nutritional blood flow, followed by RGC death. Recent research into RGC death in glaucoma has added many potential contributors to any simple formula.3 To redesign our concepts of glaucoma pathogenesis, let us expand the set of possible events leading to RGC death. This is merely an initial illustration, not a proposal of known sequence, though I believe that the stages are plausible.

In Table 1, I list 15 events leading to RGC death. The location of each event is indicated, and steps are grouped into Initiator, Promotor, or Secondary. Initiators occur early, before actual alterations in RGC themselves (this is sometimes called ‘upstream’). Promotor steps act later, within RGC (‘downstream’), and Secondary steps occur after initial RGC death, and result from it. For each step, I have presented one potential risk factor (each event could have many). A risk factor is a condition that would make this event more likely to occur. For instance, risk factors can be features of the person, of ocular cells and their extracellular matrix, of RGC geography, of the individual’s genetic endowment, or his response to altered conditions.

A detailed linear exposition allows us to dissect more carefully how and where certain risk factors might participate in the process. For example, mitochondria could be important either in the axon or at the cell body. While it could be important to know how to inhibit the caspase enzymes that carry out apoptotic cell death, these are far downstream and the process may have gone beyond therapy

Table 1. Linear conceptualization of steps in RGC death in glaucoma

by that point. Even with our present rudimentary knowledge, events in the process are much more complex than shown, and many more variables and risk factors could be involved from initial steps to the final ones. No wonder all patients with an IOP averaging 24 mmHg do not suffer visual field loss at the same rate.

The specification of events and risk factors in the pathological process provides clues to potential therapeutic interventions. For example, if an altered mitochondrial membrane potential contributes to apoptotic death, treatments could be devised and tested to block this event. Weakness of elastin or its connection to the extracellular matrix represent another possible avenue for treatment.

It has become common to speak of ‘neuroprotection’ therapy for glaucoma, or of protecting RGC by a method other than lowering IOP. But, which events are those that are interrupted in neuroprotection? Might it interrupt an Initiator event (e.g., improving nerve head blood flow)? Could it involve intervention in a Promotor, perhaps one of those within the RGC body during the induction of apoptosis? Or, does it refer to prevention of the Secondary degeneration among innocently bystanding RGC that were not primarily injured?

One disadvantage of a series of linear pathogenic events is that there are surely inter-individual variations in how the process proceeds. Some events may never happen in some glaucoma eyes. Many eyes are susceptible to glaucoma at normal IOP, so they could suffer lamina collapse (event 4) or poor blood flow in the nerve head (event 6) without the preceding events ever occurring. Furthermore, some eyes might scramble the order. It has been suggested that the death of some RGC might make the nerve head more sensitive to further injury than it was before damage. Hence, the person with poor blood flow who suffered non-glaucoma RGC loss might make glaucoma damage more likely. This puts poor autoregulation of blood flow at multiple sites along the path.

Multivariate approaches

Therefore, the linear stepwise analysis must be generalized to allow for variations in sequence, multiple effects of some events, and interactive behaviors that are non-linear. How can we understand this without hopeless complexity? The wellknown biostatistical approach called multivariate analysis is a model solution dealing with many effects all at once. An equation is constructed with a dependent variable on the left side (here it is RGC death), and on the right side of the equation are a series of variables, each of which may be related to RGC death. Risk factors are independent variables in the equation for this analysis. Spaeth was probably one of the first to use multivariate analysis for assessment of glaucoma risk factors,4 and it has been applied many other times to the clinical issues of glaucoma outcome.5

Since we are considering glaucoma pathogenesis, the dependent variable in our first formulation is RGC death, not its clinical recognition in disc analysis or field testing. If we were to use functional impairment as the analytic outcome, risk factors would include things like how reliable the subject is at field testing and confounding factors like cataract. That is a different model for a different essay.

Variables (risk factors) can be associated with RGC death in a variety of ways and can express themselves in different forms. For a multivariate model, we can treat risk factors in mathematical analogies. IOP is a familiar risk factor that we

could place in the equation in several formats, depending upon which manifestation of IOP we wish to assess. These could be the mean IOP over time, the range of diurnal fluctuation, or the amount that the optic disc surface bows back or subsides when IOP increases or decreases. These three formats could be described as the following manifestations of IOP: static (mean), dynamic (diurnal), and interactive (effect on another risk factor, here, the nerve head tissues). We may not know at present which manifestation of IOP is most important, but, in a model, we can put them all into the formula and assess their effects together or separately.

For each format, the state of the variable could be protective or detrimental to glaucoma outcome. Some variables might always be one or the other, but others could be both. For example, IOP lowering is beneficial for field outcome to a point, but hypotony maculopathy acts in the opposite direction. Interaction of one variable with another could involve the variable as the active partner (causing the change in another variable, good or bad), or the passive partner (receiving an effect for better or for worse) (Table 2).

Table 2 shows properties of the risk factor IOP, including its static and dynamic components. The way in which these elements would be incorporated into a mathematical model of glaucoma pathogenesis is indicated by the symbols in the last line

Some variables are inherently static, since they are fixed for that person: ethnicity and chromosomal gender are examples. Others provide a measure of the state of the individual, but are dynamic, e.g., age. While these are fixed for the individual at a point in time, they can be interactive with other variables. For example, young persons with systemic high blood pressure are less likely to have OAG, while persons over the age of 65 years with hypertension are more likely than average to have it.6 We could describe this as a dynamic interaction of age with hypertension relative to glaucoma; or, older age may be a surrogate for the duration of hypertension. In a model, we can deal with both simultaneously and not have to decide that one is important and not the other.

Ethnicity and gender may be variables that express the effects of groups of genes that are more common in certain races. As we determine what those genes are, the variables will be more appropriately specified (the myocilin gene may be approaching this status). Alternatively, race and gender may be important because they indicate differential environmental effects for persons of particular groups

Table 2. Mathematical description of risk factor: IOP

that make glaucoma more likely to be present or to progress. Persons of one ethnicity can have lower socioeconomic status, higher access to care, negative cultural attitudes toward glaucoma screening, or personal behaviors that make disease less likely.

For some individuals or glaucoma subtypes, one variable may dominate the process, while in other groups that variable may be subsidiary or even irrelevant. The strength of association is a way to describe the importance of a variable, since it is unlikely to be all or none. The strength can be measured empirically using criteria such as:

1.How strong is the variable’s association quantitatively? For example, a 25% lowering of IOP leads to a 40% decrease in field progression rate.

2.How consistent is the association? For example, a weak lamina cribrosa might initiate damage even at normal IOP, but in traumatic secondary glaucoma, IOP is high enough that the normal lamina structure is overpowered.

3.Is there a doseBresponse relationship? For example, the higher the IOP, the greater the chance of field loss.

4.How plausible is it that the variable is associated? For example, sunspot activity may be associated with angle-closure glaucoma, but the plausibility of a direct connection is weak.

5.Is there experimental proof that altering the variable affects the disease? For example, does a controlled clinical trial show that calcium channel blockers slow

field loss.

We can put the strength of a variable into the model by assigning it a mathematical coefficient, even making one for its static state, and one for the dynamic or interactive state, when present. It will be helpful to consider how this could be done. Consider a scatter plot of points, each defined by the values of two variables (x,y) or (IOP, RGC death). If these points all fell on one straight, perfect line, it would cross the y axis at an intercept and would have a slope. This is expressed as y = a0 + a1x, where a0 is the intercept and a1 the slope. But, real data are scattered around any perfect line. Simple linear regression analysis (the method of least squares) estimates the best fitting, straight line that would connect all the points. The slope indicated by the regression line is the coefficient that describes the form of the relationship between the dependent variable (y, RGC death) and an independent variable (x, say blood flow). If large changes in blood flow (x) cause very little increase in RGC death (y), then the slope is flat B and we describe the risk factor as weak in its association with the outcome. If the points are widely scattered, linear regression will generate a ‘best fitting’ line B but the measure called r2 judges how much of the variation in y is explained by x. If r2 is large (close to 1), then x explains a lot of the variation in y B the association is strong. Lower values of r2 indicate very little relationship between the two. So, the slope shows how much change in x there is for a change in y. The r2 shows how much confidence we have that the slope (steep or flat) represents a strong association. We could have a high r2 and a flat slope B a result showing conclusively no effect of x on y, or a steep slope and low r2 B a possible big effect of x on y, but no real confidence that the available data show a strong relationship. The best fitting line can be exponential or a power function instead of linear.

But, what if we wish to evaluate the relationship between RGC death (dependent variable, y) and many risk factors simultaneously (independent variables, x1, x2, x3, etc.)? This is what multivariate analyses do B the effects of more than one

independent variable are evaluated simultaneously. Just as linear regression assigns a slope and r2, in multivariate analysis, each variable is assigned its own coefficient and a judgment of the strength of association is made. In some multivariate methods, this is done by calculation of an odds ratio and its 95% confidence limit B the odds being the likelihood of a strong association between each dependent variable and the outcome, given the adjusting effect of data from the other variables in the equation. The point is to describe the general approach as potentially valuable for our thinking about glaucoma pathogenesis.

Frequency

While a variable may be highly associated with disease when it is present, if it is rare, its importance is minimal for most individuals. A mutation in the myocilin gene may be associated with a ten times greater chance of developing glaucoma. In multivariate analysis, this would lead to a high odds ratio, pointing to a strong association. But, what if the frequency of such mutations is low among all glaucoma patients? Consider the little town of Cornville with 1000 older adults, where 1% have glySTOP, the most frequent myocilin gene defect associated with glaucoma. Ten persons would have the mutation (1% times 1000). The prevalence of glaucoma is 2% among adults, so out of 990 persons without the mutation, there are 20 glaucoma cases, while the ten myocilin mutation carriers exhibit two cases (ten times the general prevalence of 2% = 20% of ten persons = 2). Hence, the attributable proportion related to myocilin mutation in this example is two of 22 total cases, or 9%. In one study, the proportion of those with OAG who had some myocilin mutation was about 3%. This allows speculation that a person with a mutation in this gene is actually three times more likely to have glaucoma (an odds ratio of three, not ten as in my example). Myocilin mutations might substantially increase glaucoma prevalence if many persons had them, but their effect is proportional to frequency. Thus, we should consider both the strength and frequency of a variable’s contribution. These two can be dissected if we perform the multivariate analysis with the data stratified into those who do and do not exhibit a trait, while including other important variables in both data sets.

Variables might be factors associated with the initial onset of disease, others might influence its natural (untreated) rate of progression, and yet others could affect the response to therapeutic interventions in ways that influence ultimate visual impairment. This may mean that models would be needed for incidence, progression, and therapeutic response.

Summary of multivariate approaches

In summary, to place a variable (risk factor) in a model of disease, we must know its characteristics. Does it have only static features, or dynamic ones as well? Does it potentially interact with other variables, and in that interaction is it the acting or the receiving partner? Is it a variable that is beneficial in preventing the disease, or detrimental? How strong is the association of the variable with the disease? Is the variable frequently found in the population, or is it rare? Is the variable one that influences disease onset, disease progression, response to therapy, or more than one of these?

Practical implications of multivariate analysis

Does all this mathematical talk have any practical value? There have been a number of past uses of multivariate modeling in glaucoma, e.g., trying to predict variables that are important in determining which suspects will develop glaucoma.5 The entry of data from a particular group of persons will produce an evaluation of risk factors relevant to that population, but not necessarily generalizable to other groups. For instance, in past textbooks, diabetes mellitus was listed as a risk factor for OAG, but recent population-based studies found no association between diabetes and glaucoma in black persons,7 although the relationship is probably present, but weak, in white persons. A further consideration derives from the fact that glaucoma develops late in life. If we are attempting to associate a risk factor that is present at the age of 30 years (say, pigment dispersion syndrome) with glaucoma, we have to recognize that glaucoma may not have developed yet. The subjects would have to be followed for 30-40 years in order to associate the pigment abnormality with the incidence of glaucoma.

A similar anomaly presents itself when trying to determine if persons with OAG are more likely to have a blood relative with glaucoma.8 The association differs for parents, siblings, and children. An 85-year-old proband would have long-dead parents, whose eye care and status would be problematic. Siblings and children of this person would have the chance of being old enough to develop glaucoma. Substitute a 40-year-old subject, and her parents would be old enough to have developed glaucoma and may be alive and diagnosed in a way that would be documentable. But, siblings and children of this person would have a low chance of having glaucoma now, even if they were going to develop it later. When OAG subjects are divided (stratified) into those who already know that they have the disease and those who have just found out, those already aware of their diagnosis are more likely to report a positive family history than the newly diagnosed. This probably results from their being told to have family members examined. The risk can only be more accurately assessed by examining all living family members.9

Mathematical models of glaucoma pathogenesis would have inherent limitations. By entering a variable into the model, we can value its contribution, but cannot ascribe either the truly causal nature of its association, or the positional order of its action. And, where interactive terms are introduced, the specific contribution of a variable becomes even more abstruse. While some event orders are obvious (e.g., death of RGC cannot be the start of the process, cause must precede effect), we will probably never understand how all the pieces fit together. Furthermore, individual human heterogeneity requires consideration. In one person, eight factors may be needed to lead to glaucoma damage, while in another, only four will produce the injury B and they may be different variables in these two patients.

Necessary and sufficient components

What causes influenza? While we could answer quickly “the influenza virus”, sufficient virus must be inoculated into the respiratory tract to proliferate, and the immune system must be ineffective in preventing it. In a brilliant analysis of what constitutes a cause of disease, the epidemiologist Rothman distinguished component causes from sufficient cause.10 In persons who develop influenza, he labels virus

and an immune system failure as component causes, which in a simple formulation would together make up a sufficient cause. Note that if a person is run down by a truck five minutes after exposure to the virus, there will not be sufficient induction time to allow the final component cause to lead to the effect. In fact, the induction time is different for each component cause, long for those acting early (upstream) and zero for the final component cause in a set of sufficient cause. The induction time for the myocilin gene mutations associated with glaucoma appears to be more than 20 years. Why does glaucoma not develop earlier? Possibly, other component causes must follow the genetic defect in time, ultimately reaching sufficient cause.

There will surely be more than one sufficient cause, made up of component causes that are either shared or unique to that sufficient cause set. Each sufficient cause consists of a minimally sufficient set of components (none is extraneous). Note how this approach deals with the weakness of the linear model in Table 2, which assumes that all persons go through the same set of steps in the same order. The diagrams in Figure 1 show sufficient causes, each made up of four component causes. Disease would only happen in each of the three conditions if all its components were present. Consider what would be true if glaucoma were caused by these three sufficient cause sets (each case of glaucoma was a result of one set, and all glaucoma cases were caused by one of the three sets). Component cause A might be defective autoregulation of the optic nerve head blood flow. What proportion of all cases are caused by A? All of them, since, in this hypothetical example, A is a component cause in all sufficient cause examples. That makes A into a necessary cause. On the other hand, if D stands for IOP, then it would play a role in glaucoma caused by the first two sets, but not the third one, and it would be a component in some cases, but not a necessary cause for any case of glaucoma.

Fig. 1. The component risk factors (causes) that make up a theoretical sufficient cause set for glaucoma in three different types of patients are given in the three schematic circles. In each case, there are four component causes for each patient, although only one is present in all three scenarios (making it a necessary cause).

Above we discussed the frequency of a risk factor (here a component cause) and its effect on our view of its participation in the disease. Consider that component cause E is weak elastin in the optic nerve head lamina and that components A, B, C, and D are relatively common in the population, but E is rare (say Ehlers-Danlos syndrome). Its sufficient cause would also be rare. But, if we compare the risk of glaucoma among those with E to those without E, the relative risk is much higher. However, components A and B would have smaller differences in risk among those with and without the traits. Thus, E(lastin) may be a biological cause of glaucoma, but its frequency in a population, and the prevalence of other compo-

nent and sufficient causes, will affect its apparent strength of association (as shown above for myocilin).

Ascertainment bias

Prevalence of a component must be evaluated in the context of who we wish to evaluate with a disease. Those who voluntarily come to a doctor’s office are not representative of the overall population with respect to many glaucoma-related issues. They are more likely to have an affected family member, and more often have higher IOP, diabetes, and myopia. If we wish to know the strength of association for component causes of glaucoma among those who can afford health care and have the motivation to come to the office, then it is fine to analyze a case series from available charts. This should be considered only a first, exploratory step, if we ideally wish to know the nature of glaucoma in all persons. An example is the interesting observation by Airaksinen and Tuulonen that those with glaucoma at lower IOP have larger disc diameters than those with higher IOP glaucoma. I suspected (as did these authors) that their finding might have been influenced by ascertainment bias. In this case, the bias would arise because eyes with larger disc diameter would have larger physiological cup/disc ratios, and would be more likely to be referred to an ophthalmologist B regardless of their IOP. Those with higher IOP would be referred anyway, independent of cup/disc ratio, thereby producing more persons with larger disc diameter among those who actually turn out to have glaucoma among the lower IOP group. To unmask this bias, we evaluated the disc diameter of those with OAG in the Baltimore Survey compared to their IOP. In fact, there is a modest tendency for a population-based group of glaucoma subjects to have larger disc diameter than those who do not have glaucoma.11 This confirms the logical idea that a larger disc would resist physical deformation less well than a small disc. But, the discs of those with lower IOP were not larger than higher IOP persons among those with glaucoma, suggesting that the clinic-based data were subject to the ascertainment bias effect. The populationbased Blue Mountains Eye Study in Australia found the same association as the Baltimore study, but the association was made stronger and more generalizable.

Population-based studies are frequently held up as the ultimate standard to debunk findings derived from other settings. This should be examined critically and not taken to the extreme. While we should examine persons randomly selected from the overall population of interest in order to know best what causes a disease in that group, population-based studies suffer from weaknesses of their own. One frequent problem in population studies for glaucoma is the low prevalence of the disease, which leads to a small number of actual cases for study. In the original Baltimore Survey, the optic disc was available to be examined in about 100 black and 40 white persons with defined OAG. Clinic-based studies from large centers can evaluate hundreds of cases. Data unselected with respect to a population are useful, but larger samples give greater confidence that, if a difference is present, it will be detected.

Conclusions

Glaucoma pathogenesis should not be viewed simplistically and ‘one cause theories’ belong in the 20th century. The analogy of multivariate regression provides a way of thinking about the many factors that interact and play a role in pathogenesis, as well as an actual analytic approach to data derived from laboratory or clinical studies. We should not be surprised if some persons apparently have the same disease clinically, but different sufficient cause. Component causes have several possible features (static, dynamic, interactive), and occur in a variety of sequences. The relative frequency of a component cause, its induction period, and how we ascertain its presence, will affect the apparent strength of its association with glaucoma.

Pathogenic research requires epidemiological studies to test for likely associations and their frequency and strength. The role of laboratory investigations is to use model systems that change one variable, and that can measure the effect of so doing. Clinical trials in humans allow us to test for the therapeutic benefit of altering a risk factor in order to improve the relevant outcome for our patients, the preservation of their quality of life.

References

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8.Tielsch JM, Katz J, Sommer A, Quigley HA, Javitt J: Family history and risk of primary open angle glaucoma: the Baltimore Eye Survey. Arch Ophthalmol 112:69-73, 1994

9.Wolfs RC, Klaver CC, Ramrattan RS, Van Duijn CM, Hofman A, De Jong PT: Genetic risk of primary open-angle glaucoma: population-based familial aggregation study. Arch Ophthalmol 116:1640-1645, 1998

10.Rothman KJ: Modern Epidemiology. Boston/Toronto: Little, Brown & Co 1986

11.Quigley HA, Varma R, Tielsch JM, Katz J, Sommer A, Gilbert DL: The relationship between optic disc area and open-angle glaucoma: the Baltimore Eye Survey. J Glaucoma 8:347-352, 1999